code: def forward(self, data):
      preprocess_output = self.preprocess(data)
      inference_output = self.inference(preprocess_output)
      postprocess_output = self.postprocess(inference_output)
      return postprocess_output
  
imports: import torch
  import torch.nn
  import warnings
  import random
  import numpy
  from torch.utils.data import TensorDataset, DataLoader
  import numpy
  import multiprocessing
  import warnings
  import math
label_desc: Use image node_id 2 for calling the ExportForecastingPipeline obj's underlying member method code with example usage: obj.forward(data) and returns: postprocess_output